1 September 2004 Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model A. Introduction and assumptions The classical normal linear regression model can be written as (1) or (2) where xtN is the tth row of the matr
Chapter 10
Random Regressors and
Moment-Based Estimation
Principles of Econometrics, 4th
Edition
Chapter 10: Random Regressors and
Moment-Based Estimation
Page 1
Chapter Contents
10.1 Linear Regression with Random xs
10.2 Cases in Which x and e are Corr
Chapter 4
Prediction, Goodness-of-fit, and
Modeling Issues
Walter R. Paczkowski
Rutgers University
Principles of Econometrics, 4th
Edition
Chapter 4: Prediction, Goodness-of-fit, and Modeling Issues
Page 1
Chapter Contents
4.1 Least Square Prediction
4.
Chapter 7
Using Indicator Variables
Walter R. Paczkowski
Rutgers University
Principles of Econometrics, 4th
Edition
Chapter 7: Using Indicator Variables
Page 1
Chapter Contents
7.1 Indicator Variables
7.2 Applying Indicator Variables
7.3 Log-linear Mod
Chapter 8
Heteroskedasticity
Walter R. Paczkowski
Rutgers University
Principles of Econometrics, 4th
Edition
Chapter 8: Heteroskedasticity
Page 1
Chapter Contents
8.1 The Nature of Heteroskedasticity
8.2 Detecting Heteroskedasticity
8.3 Heteroskedastic
University of New Mexico
Economics Department
ECON-309 Introduction to Statistics and Econometrics
Assignment 2
Instructions: This assignment is due on Friday, September 9th. You may submit through Learn up until midnight, submit a paper
copy in class or
Economics 671
Problem Set #2
Multivariate and Conditional Probability
(1) Consider the joint density function
f (x, y) =
2 exp([x + y])
0
for 0 x y, 0 y
otherwise .
(a) Show that this is a valid bivariate density function.
(b) Derive the marginal densitie
Economics 671 Problem Set #5 Central Limit Theorem
(1) Suppose that X1 , X2 , , Xn denote an iid sample from an exponential distribution with density function: f (Xi ) = 1 exp(xi 1 ), xi 0 i. (1a) Using the moment generating function approach, derive the
Economics 671 Problem Set #3 Special Distributions and Changes of Variable
(1a) Suppose that f (x) = 2x, 0 x 1.
Describe how you can generate draws from this distribution. (1b) Using MATLAB and your solution in (1a), write a program that generates 10,000
Economics 671 Solutions: Problem Set #2 Multivariate and Conditional Probability
(1) (a) First, note that the density is always positive over its support. Moreover,
0 x
2 exp[(x + y)]dydx = 1
(which you can verify through some rather simple integration)
Economics 671 Solutions: Problem Set #4 Convergence Concepts
(1) Fix
> 0. We seek to show that
n 2 lim Pr |Xn - a2 | >
= 0.
Choose a > 0 such that 2 + 2|a| <
2 Pr |Xn - a2 | >
and write the above probability as
2 2 = Pr |Xn - a2 | > , |Xn - a| + Pr |Xn -
Economics 671 Problem Set #1 Univariate Probability
(1) Casella and Berger, 2.24: Compute E(x) and Var(X) for each of the following probability distributions (a) f (x) = axa-1 , 0 < x < 1, a > 0.
1 (b) f (x) = n , x = 1, 2, , n where n is an integer.
(Not
Economics 573 Problem Set 4 Fall 2002 Due: 20 September 1. Ten students selected at random have the following "final averages" in physics and economics. Students Physics Economics a. 1 66 75 2 70 70 3 50 65 4 80 88 5 60 60 6 70 85 7 55 60 8 90 97 9 75 82
Economics 573 Problem Set 5 Fall 2002 Due: 4 October 2002 1. In random sampling from any population with E(X) = : and Var(X) = F2, show (using Chebyshev's inequality) that sample mean converges in probability to :. 2. In random sampling from any populatio
Economics 671 Solutions: Problem Set #2 Special Distributions and Changes of Variable
(1) MATLAB code for this portion of the problem set has been provided. Note that the inverse transform method suggests that u, where u U (0, 1) will provide a draw from
Economics 671 Problem Set #4 Convergence Concepts
2 (1) Suppose that Xn a for some constant a and consider the sequence Yn = Xn . Show directly
p
[that is, without making use of the theorem discussed in class that g(Xn ) g(a) for a continuous function g]
Chapter 9
Regression with Time Series
Data:
Stationary Variables
Walter R. Paczkowski
Rutgers University
Principles of Econometrics, 4th
Edition
Chapter 9: Regression with Time Series Data:
Stationary Variables
Page 1
Chapter Contents
9.1 Introduction
9